Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application

Sensors
Sigfredo FuentesClaudia Gonzalez Viejo

Abstract

Cocoa is an important commodity crop, not only to produce chocolate, one of the most complex products from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried, and ground to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm, with the aroma profile, considering six main aromas, as targets. The ANN model rendered high accuracy (correlation coefficient (R) = 0.82; mean squared error (MSE) = 0.09) with no overfitting. The model was then applied to an aerial image of the whole cocoa field studied to produce canopy vigor, and aroma profile maps up to the tree-by-tree scale. The tool developed could significantly aid the canopy management practices in cocoa trees, which have a direct effect on cocoa quality.

References

Nov 20, 2004·TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·James A SaundersAlaa A Hemeida
Sep 13, 2008·Critical Reviews in Food Science and Nutrition·Emmanuel Ohene AfoakwaAngela Ryan
May 1, 2012·Food Chemistry·J Rodriguez-CamposE Lugo-Cervantes
Feb 16, 2019·Frontiers in Plant Science·Johann Martínez-LüscherSahap Kaan Kurtural
Jan 1, 2016·Comprehensive Reviews in Food Science and Food Safety·Ana Clara AprotosoaieAnca Miron

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Software Mentioned

Matlab ®
CIELab
SAS
VitiCanopy
Google Earth Pro
VitiCanopy App
Matlab

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